Lung diseases encompass a diverse range of disorders, including Idiopathic Pulmonary Fibrosis (IPF), Chronic Obstructive Pulmonary Disease (COPD), bronchopulmonary dysplasia, pulmonary hypertension, asthma, and cancer. These conditions present complex diagnostic and therapeutic challenges, often manifesting with varied patterns of tissue alteration and anatomic distribution. Recent advances in genetics, omics technologies, and imaging have begun to unravel the complex biological underpinnings of these lung diseases, highlighting the roles of genetic predisposition, environmental exposures, cell communication, cellular aging, and development. However, a detailed understanding of how disease processes vary spatially within lung tissues across different pathologies remains elusive, underscoring the need for specialized spatial analytical approaches.

Spatial analysis techniques preserve and interrogate the architecture of lung tissue, allowing researchers to study not just the presence of specific cells or molecules, but their precise localization and interaction within the tissue microenvironment. These techniques include spatial transcriptomics, imaging mass cytometry, multiplex immunofluorescence, and artificial intelligence-based histological mapping. Such approaches have illuminated the heterogeneity and complexity of lung disease, revealing, for example, the spatial distribution of fibroblastic foci in IPF, immune cell niches in lung cancer, and early changes in small airway architecture in COPD.

In lung cancer, the combination of single-cell and spatial transcriptomics has revealed significant spatial heterogeneity in immune cell infiltration and tumor-immune interactions, highlighting potential therapeutic targets. Similarly, imaging mass cytometry has provided a detailed view of the fibrotic progression in chronic lung allograft dysfunction. Spatial transcriptomics has also been employed to map molecular alterations in distinct alveolar niches in IPF, advancing our understanding of disease progression and potentially guiding targeted treatment strategies.

Spatial context not only helps characterize disease mechanisms and progression but also supports the development of new diagnostic and therapeutic tools. For example, spatial biomarkers can offer prognostic insight or inform patient stratification in clinical trials. Integration of multi-omics data with spatially resolved imaging allows a comprehensive analysis of molecular and cellular networks in diseased lungs.

This Research Topic aims to enhance the understanding and management of lung disease by disseminating knowledge, discoveries, and best-practices of advanced spatial analysis techniques. By focusing on the unique anatomic attributes of lung tissue associated with various pulmonary disorders, the goal is to highlight emerging themes in respiratory disease research while underscoring the contextual anatomy of the lung. Employing sophisticated imaging and omics technologies allows researchers to visualize and analyze molecular and cellular interactions within specific lung regions affected by disease.

In pursuit of broadening our knowledge on the spatial aspects of lung diseases, this Research Topic is open to submissions that showcase innovative research and technology in the following areas:

o Spatially-resolved transcriptomics approaches across different lung conditions;
o Imaging techniques for detailed lung disease staging;
o Single-cell and single-nucleus genomic and epigenetic mapping;
o Techniques for dissecting lung tissue into specific compartments for detailed study;
o Proteomic and metabolomic landscape mapping in diseased lung tissues;
o Integrative methods for combining spatial omics data and imaging within anatomical lung structures;
o Novel diagnostic tools based on spatial analysis in lung disease research.

Are you interested in contributing a manuscript to this Research Topic?

Spatial Approaches to Understanding Lung Health and Disease Across Lifespan